A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints

25 June 2017

Papers citing "A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints"

9 / 9 papers shown

Title
Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms Hesameddin Mohammadi Meisam Razaviyayn Mihailo R. Jovanović 18 7 0 24 Sep 2022
Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation Xing-ming Guo Bin Hu 11 2 0 20 Apr 2022
Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods Xing-ming Guo Bin Hu OffRL 16 3 0 14 Feb 2022
Global Convergence Using Policy Gradient Methods for Model-free Markovian Jump Linear Quadratic Control Santanu Rathod Manoj Bhadu A. De 16 8 0 30 Nov 2021
Self-Healing First-Order Distributed Optimization Israel L. Donato Ridgley R. Freeman K. Lynch 13 4 0 05 Apr 2021
Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems Joao Paulo Jansch-Porto Bin Hu Geir Dullerud 25 35 0 10 Feb 2020
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 9 3 0 22 Jun 2019
Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs Bin Hu Peter M. Seiler Laurent Lessard 16 38 0 03 Nov 2017
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 139 1,199 0 16 Aug 2016